Particle Identification in the NA48 Experiment Using Neural ...s and neutral Hyperons decays –...
Transcript of Particle Identification in the NA48 Experiment Using Neural ...s and neutral Hyperons decays –...
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Particle Identification in the NA48 Experiment Using
Neural Networks
L. Litov
University of Sofia
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Introduction
� NA 48 detector is designed for measurement of the CP-violation parameters in the K0 – decays –successfully carried out.
� Investigation of rare K0 s and neutral Hyperons decays – 2002
� Search for CP-violation and measurement of the parameters of rare
charged Kaon decays – 2003
� A clear particle identification is required in order to suppress the background
� In K – decays – µ, πµ, πµ, πµ, π and e
� Identification of muons do not cause any problems
� We need as good as possible e/ππππ - separation
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
NA48 detector
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Introduction
�2003 Program for a precision measurement of Charged Kaon Decays Parameters
�Direct CP – violation in ,
�Ke4 -
�Scattering lengths
�Radiative decays
mπππ ±±± →K ±± → πππ 00K
)(ννππ ±±± → eKm
2
0
0
0 ,aa
γππγγγπγγπ 0,, ±±±±±± →→→ KKK
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Introduction
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
E /p
K3π Background
�The standart way to separate
e and π π π π is to use E/p�ΕΕΕΕ − energy deposited by
particle in the EMcalorimeter
�p – particle momentum�cut at E/p > 0.9 for e
�cut at E/p< 0.8 for ππππ
signal
Kπ3 background
control region signal
Introduction
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Sensitive Variables
� Difference in development of e.m. and hadron showers
� Lateral development
� EM calorimeter gives information for lateral development
� From Liquid Kripton Calorimeter (LKr)
� E/p
� Emax/Eall, RMSX, RMSY
� Distance between the track entry point and the associated shower
� Effective radius of the shower
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Sensitive variables - E/p
�MC simulation
�A correct simulation of the energy deposed by pions in the EM calorimeter - problem for big E/p
�It is better to use experimental events
E/p distribution
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Sensitive variables - RMS
RMS of the electromagnetic cluster
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Distance
Distance between track entry point and center of the EM cluster
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Sensitive variables - Emax/Eall, Reff
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
MC
�To test different possibilities we have used:
�Simulated Ke3 decays – 1.3 M
�Simulated single e and π – 800 K π and 200 K e
�Using different cuts we have obtained
�Relatively to E/p < 0.9 cut
�Keeping > 95 %
�Using Neural Network it is possible to reach e/π separation:
�Relatively to E/p < 0.9 cut
�Keeping > 98%
�The background from ~ 0.1%
2107.15 −→ ×=e
eff
πεe
effε
mπππ ±±± →K
e
effε
2100.2 −→ ×<e
eff
πε
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Neural Network
Powerful tool for:
�Classification of particles
and final states
�Track reconstruction
�Particle identification
�Reconstruction of invariant masses
�Energy reconstruction in calorimeters
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Neural Network
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Neural Network
�Multi-Layer-Feed Forward network consists of:
�Set of input neurons
�One or more layers of hidden neurons
�Set of output neurons
�The neurons of each layer are connected to the ones to the subsequent layer
�Training
�Presentation of pattern
�Comparison of the desired output with the actual NN output
�Backwards calculation of the error and adjustment of the weights
�Minimization of the error function
2)(2
1∑ −=
j
jj otE
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
NN 10-30-20-2-1
Input layer
Hiden layers
Output layer
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Neural Network
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Experimental data
� E/pi separation – to teach and test the performance of NN
We have used experimental data from two different runs
�Charged kaon test run # 1 2001
�electrons from
�pions from
� run 2001
�electrons from
�pions from
γπππ −+±±± →→ eeK0
mπππ ±±± →K
00 πππ −+→K
40eK
νπ meK
±→0
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Charged run
Pions
�Track momentum > 3 GeV
�Very tight selection
�Track is chosen randomly
�Requirement – E/p < 0.8 for the other two tracks
mπππ ±±± →K
mπππ→ ±±±K
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
γπππ −+±±± →→ eeK0
Selection :
�3 tracks
�Distance between each two tracks > 25 cm
�All tracks are in HODO and MUV acceptance
�Selecting one of the tracks randomly
�Requirement – two are e (E/p > 0.9) and π (E/p < 0.8)
�The sum of tracks charges is 1
�Three-track vertex CDA < 3 cm
�One additional in LKr, at least 25 cm away from the tracks
�0.128 GeV < < 0.140 Gev
�0,482 GeV < < 0.505 GeV
±
0πm
km
γ
Electron selection
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
γπππ −+±±± →→ eeK0
Electron selection
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Charged run
E/p and momentum distributions
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Charged run NN output
�Out � 0 for ππππ
�If out > cut – e
�If out < cut - ππππ
NN output
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Charged run NN performance
�Net: 10-30-20-2-1
�Input: E/p, Dist, Rrms, p, RMSx, RMSy, dx/dz, dy/dz, DistX, DistY
�Teaching: 10000 π - , 5000 e -mπππ ±±± →K γπππ −+±±± →→ eeK
0
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
)(ννππ ±±± → eKm
π3
effM
π3
effM
E/p
E/p
E/p > 0.9
Non symmetric
E/p distribution
E/p > 0.9
out > 0.9
Symmetric E/p
distribution
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
E/p
E/p
)(ννππ ±±± → eKm
out > 0.9
E/p distribution
�out > 0.8
�E/p distribution
�There is no significant change in the parameters
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
)(ννππ ±±± → eKm
There is a good agreement between MC and Experimental distributions
π3
effM
MCEXP
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
reconstruction with NN
Decay
�Significant background comes from
�when one π is misidentified as an e
�Teaching sample:
�Pions - from , 800 K events
�Electrons - from , 22 K events
νππ 00 meK
±→
00 πππ −+→K
νπ meK
±→0
00 πππ −+→K
νππ 00 meK
±→
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
reconstruction with NNνππ 00 meK
±→
NN outputE/p distribution
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
reconstruction with NNνππ 00 meK
±→
ππππ rejection factore identification efficiency
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Ke4 run NN performance
�Net: 10-30-20-2-1
�Input: E/p, Dist, Rrms, p, RMSx, RMSy, dx/dz, dy/dz, DistX, DistY
�Teaching: 10000 π - , 5000 e - νπ meK ±→000 πππ −+→K
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
reconstruction with NNνππ 00 meK
±→
222 )5.2
498()
7
6( 3
MeV
MeVM
MeV
MeVpR
Kt−
+−
= π
πππ
Ke3
Ke4
1/REllipse
m3π / GeV
222 )5.2
498()
7
6( 3
MeV
MeVM
MeV
MeVpR
Kt−
+−
= π
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
recognition with NNνππ 00 meK
±→
NN output
versus 1/R
•the background
from K3π π π π is
clearly separated
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
νππ 00 meK
±→
e/π Neural Network
Performance
• no bkg subtraction!
• using nnout > 0.9 cut
• works visibly very well
• but what about bkg?
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
reconstruction with NNνππ 00 meK
±→
e/π Neural NetworkBackground
• extending range of 1/R to 5
• obviously there is bkg!
Ke4 MC
E /p
1/R
1/R
without NN with NN
E /p E /p
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
νππ 00 meK
±→ reconstruction with NN
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Performance
• background is fitted both
with and without NN
• ratio R (rejection factor)
is measure of performance
νππ 00 meK
±→
e/π Neural Network
reconstruction with NN
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Optimization
• goal: optimize the cut
values for nnout and 1/R
1/R
nnout
nnout
NN rejection factor background
Signal NN efficiency 1/R
νππ 00 meK
±→
e/π Neural Network
reconstruction with NN
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Optimization
e/π Neural Network
• value to minimize:
combined statistical and
systematical error
• statistical error goes with
N-½
• systematical error grows
with background
sig
bkg
N
1 c ⋅+=σ1/R
nnout
σ
statistical limitssystematical limits
νππ 00 meK
±→ reconstruction with NN
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
νππ 00 meK
±→
e/π Neural Network
Performance
• background can be reduced
at level 0.3 %
•Ke4 reconstruction
efficiency at level 95%
reconstruction with NN
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Conclusions – e/pi separation
�e/π separation with NN has been tested on experimental data
�For charged K run we have obtained:
�Relatively to E/p < 0.9 cut
�At 96%
�A correct Ke4 analysis can be done without additional detector (TRD)
�Background can be reduced at the level of ~ 1%
� ~ 5 % of the Ke4 events are lost due to NN efficiency
�For Ke4 run we have obtained:
�Rejection factor ~ 38 on experimental data
�Background ~ 0.3% at 95%
~effε
2104.3~ −→ ×e
eff
πε
~effε
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L. Litov Particle Identification in the NA48 Experiment Using Neural Networks ACAT’ 2002
Conclusions – NN analysis
�Additionally Neural Network for Ke4 recognition has been developed
�The combined output of the two NN is used for selection of Ke4 decays
�NN approach leads to significant enrichment of the Ke4 statistics ~2 times
�This work was done in collaboration with
C. Cheshkov, G. Marel, S. Stoynev and L. Widhalm